Overview

Dataset statistics

Number of variables23
Number of observations379068
Missing cells32476
Missing cells (%)0.4%
Duplicate rows247
Duplicate rows (%)0.1%
Total size in memory66.5 MiB
Average record size in memory184.0 B

Variable types

Categorical11
DateTime2
Text6
Numeric4

Alerts

Billing grp has constant value "LFS"Constant
Channel_lfs has constant value "LFS"Constant
Dataset has 247 (0.1%) duplicate rowsDuplicates
Brand is highly imbalanced (97.0%)Imbalance
Channel is highly imbalanced (53.4%)Imbalance
Region has 14357 (3.8%) missing valuesMissing
Franchisee store has 18119 (4.8%) missing valuesMissing
Quantity is highly skewed (γ1 = 29.65043719)Skewed

Reproduction

Analysis started2024-06-02 16:36:58.680830
Analysis finished2024-06-02 16:37:18.707935
Duration20.03 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

Year
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.9 MiB
2022-23
196694 
2023-24
182374 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters2653476
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-23
2nd row2022-23
3rd row2022-23
4th row2022-23
5th row2022-23

Common Values

ValueCountFrequency (%)
2022-23 196694
51.9%
2023-24 182374
48.1%

Length

2024-06-02T22:07:18.812571image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-02T22:07:19.059419image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2022-23 196694
51.9%
2023-24 182374
48.1%

Most occurring characters

ValueCountFrequency (%)
2 1333898
50.3%
0 379068
 
14.3%
- 379068
 
14.3%
3 379068
 
14.3%
4 182374
 
6.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2653476
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 1333898
50.3%
0 379068
 
14.3%
- 379068
 
14.3%
3 379068
 
14.3%
4 182374
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2653476
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 1333898
50.3%
0 379068
 
14.3%
- 379068
 
14.3%
3 379068
 
14.3%
4 182374
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2653476
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 1333898
50.3%
0 379068
 
14.3%
- 379068
 
14.3%
3 379068
 
14.3%
4 182374
 
6.9%

Month
Date

Distinct51
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.9 MiB
Minimum2022-04-01 00:00:00
Maximum2024-01-01 00:00:00
2024-06-02T22:07:19.429738image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-02T22:07:19.719628image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Month Key
Categorical

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.9 MiB
Dec
41985 
Oct
36916 
Jul
34059 
Aug
33924 
May
33379 
Other values (7)
198805 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1137204
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowApr
2nd rowApr
3rd rowApr
4th rowApr
5th rowApr

Common Values

ValueCountFrequency (%)
Dec 41985
11.1%
Oct 36916
9.7%
Jul 34059
9.0%
Aug 33924
8.9%
May 33379
8.8%
Apr 32669
8.6%
Jun 32192
8.5%
Jan 31896
8.4%
Nov 31293
8.3%
Sep 28868
7.6%
Other values (2) 41887
11.0%

Length

2024-06-02T22:07:19.945929image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dec 41985
11.1%
oct 36916
9.7%
jul 34059
9.0%
aug 33924
8.9%
may 33379
8.8%
apr 32669
8.6%
jun 32192
8.5%
jan 31896
8.4%
nov 31293
8.3%
sep 28868
7.6%
Other values (2) 41887
11.0%

Most occurring characters

ValueCountFrequency (%)
u 100175
 
8.8%
e 98944
 
8.7%
J 98147
 
8.6%
a 79071
 
7.0%
c 78901
 
6.9%
A 66593
 
5.9%
n 64088
 
5.6%
p 61537
 
5.4%
M 47175
 
4.1%
r 46465
 
4.1%
Other values (12) 396108
34.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1137204
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
u 100175
 
8.8%
e 98944
 
8.7%
J 98147
 
8.6%
a 79071
 
7.0%
c 78901
 
6.9%
A 66593
 
5.9%
n 64088
 
5.6%
p 61537
 
5.4%
M 47175
 
4.1%
r 46465
 
4.1%
Other values (12) 396108
34.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1137204
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
u 100175
 
8.8%
e 98944
 
8.7%
J 98147
 
8.6%
a 79071
 
7.0%
c 78901
 
6.9%
A 66593
 
5.9%
n 64088
 
5.6%
p 61537
 
5.4%
M 47175
 
4.1%
r 46465
 
4.1%
Other values (12) 396108
34.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1137204
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
u 100175
 
8.8%
e 98944
 
8.7%
J 98147
 
8.6%
a 79071
 
7.0%
c 78901
 
6.9%
A 66593
 
5.9%
n 64088
 
5.6%
p 61537
 
5.4%
M 47175
 
4.1%
r 46465
 
4.1%
Other values (12) 396108
34.8%

QTR
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.9 MiB
Q3
110194 
Q1
98240 
Q2
96851 
Q4
73783 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters758136
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowQ1
2nd rowQ1
3rd rowQ1
4th rowQ1
5th rowQ1

Common Values

ValueCountFrequency (%)
Q3 110194
29.1%
Q1 98240
25.9%
Q2 96851
25.5%
Q4 73783
19.5%

Length

2024-06-02T22:07:20.211133image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-02T22:07:20.393602image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
q3 110194
29.1%
q1 98240
25.9%
q2 96851
25.5%
q4 73783
19.5%

Most occurring characters

ValueCountFrequency (%)
Q 379068
50.0%
3 110194
 
14.5%
1 98240
 
13.0%
2 96851
 
12.8%
4 73783
 
9.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 758136
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Q 379068
50.0%
3 110194
 
14.5%
1 98240
 
13.0%
2 96851
 
12.8%
4 73783
 
9.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 758136
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Q 379068
50.0%
3 110194
 
14.5%
1 98240
 
13.0%
2 96851
 
12.8%
4 73783
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 758136
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Q 379068
50.0%
3 110194
 
14.5%
1 98240
 
13.0%
2 96851
 
12.8%
4 73783
 
9.7%

Region
Categorical

MISSING 

Distinct5
Distinct (%)< 0.1%
Missing14357
Missing (%)3.8%
Memory size2.9 MiB
West
105498 
South
90644 
North
84063 
East
68152 
NORTH
16354 

Length

Max length5
Median length5
Mean length4.5238696
Min length4

Characters and Unicode

Total characters1649905
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSouth
2nd rowSouth
3rd rowSouth
4th rowWest
5th rowSouth

Common Values

ValueCountFrequency (%)
West 105498
27.8%
South 90644
23.9%
North 84063
22.2%
East 68152
18.0%
NORTH 16354
 
4.3%
(Missing) 14357
 
3.8%

Length

2024-06-02T22:07:20.529316image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-02T22:07:20.767408image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
west 105498
28.9%
north 100417
27.5%
south 90644
24.9%
east 68152
18.7%

Most occurring characters

ValueCountFrequency (%)
t 348357
21.1%
o 174707
10.6%
h 174707
10.6%
s 173650
10.5%
W 105498
 
6.4%
e 105498
 
6.4%
N 100417
 
6.1%
S 90644
 
5.5%
u 90644
 
5.5%
r 84063
 
5.1%
Other values (6) 201720
12.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1649905
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 348357
21.1%
o 174707
10.6%
h 174707
10.6%
s 173650
10.5%
W 105498
 
6.4%
e 105498
 
6.4%
N 100417
 
6.1%
S 90644
 
5.5%
u 90644
 
5.5%
r 84063
 
5.1%
Other values (6) 201720
12.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1649905
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 348357
21.1%
o 174707
10.6%
h 174707
10.6%
s 173650
10.5%
W 105498
 
6.4%
e 105498
 
6.4%
N 100417
 
6.1%
S 90644
 
5.5%
u 90644
 
5.5%
r 84063
 
5.1%
Other values (6) 201720
12.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1649905
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 348357
21.1%
o 174707
10.6%
h 174707
10.6%
s 173650
10.5%
W 105498
 
6.4%
e 105498
 
6.4%
N 100417
 
6.1%
S 90644
 
5.5%
u 90644
 
5.5%
r 84063
 
5.1%
Other values (6) 201720
12.2%
Distinct700
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size2.9 MiB
Minimum2022-04-01 00:00:00
Maximum2024-02-29 00:00:00
2024-06-02T22:07:20.914409image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-02T22:07:21.067317image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct191
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.9 MiB
2024-06-02T22:07:21.368236image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length10
Median length9
Mean length8.5804157
Min length7

Characters and Unicode

Total characters3252561
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)< 0.1%

Sample

1st rowNFM03DQ1
2nd rowNDFM13PD1
3rd rowNFM05DQ1
4th rowFW19PC1
5th rowNEFW05PGC
ValueCountFrequency (%)
nffm01pgc 17026
 
4.5%
nfm01dq1 16928
 
4.5%
nffm01cl2 15355
 
4.1%
nffw01cl2 13397
 
3.5%
nefw11pd1 12613
 
3.3%
nffw02pfc 12260
 
3.2%
nfw01dq1 11827
 
3.1%
nfm05dq1 11229
 
3.0%
nfm03dq1 10507
 
2.8%
nffm14ph1 10154
 
2.7%
Other values (181) 247772
65.4%
2024-06-02T22:07:21.762179image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
F 600093
18.4%
1 448439
13.8%
N 334748
10.3%
P 275296
8.5%
0 265721
8.2%
M 201580
 
6.2%
W 160465
 
4.9%
D 154273
 
4.7%
C 147558
 
4.5%
2 137695
 
4.2%
Other values (15) 526693
16.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3252561
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F 600093
18.4%
1 448439
13.8%
N 334748
10.3%
P 275296
8.5%
0 265721
8.2%
M 201580
 
6.2%
W 160465
 
4.9%
D 154273
 
4.7%
C 147558
 
4.5%
2 137695
 
4.2%
Other values (15) 526693
16.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3252561
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F 600093
18.4%
1 448439
13.8%
N 334748
10.3%
P 275296
8.5%
0 265721
8.2%
M 201580
 
6.2%
W 160465
 
4.9%
D 154273
 
4.7%
C 147558
 
4.5%
2 137695
 
4.2%
Other values (15) 526693
16.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3252561
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F 600093
18.4%
1 448439
13.8%
N 334748
10.3%
P 275296
8.5%
0 265721
8.2%
M 201580
 
6.2%
W 160465
 
4.9%
D 154273
 
4.7%
C 147558
 
4.5%
2 137695
 
4.2%
Other values (15) 526693
16.2%

Quantity
Real number (ℝ)

SKEWED 

Distinct56
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1827957
Minimum-18
Maximum142
Zeros194
Zeros (%)0.1%
Negative1127
Negative (%)0.3%
Memory size2.9 MiB
2024-06-02T22:07:22.092057image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-18
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum142
Range160
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.90457777
Coefficient of variation (CV)0.76477939
Kurtosis2476.1974
Mean1.1827957
Median Absolute Deviation (MAD)0
Skewness29.650437
Sum448360
Variance0.81826094
MonotonicityNot monotonic
2024-06-02T22:07:22.317148image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 331365
87.4%
2 35465
 
9.4%
3 6681
 
1.8%
4 1985
 
0.5%
-1 1088
 
0.3%
5 784
 
0.2%
6 434
 
0.1%
7 252
 
0.1%
0 194
 
0.1%
8 160
 
< 0.1%
Other values (46) 660
 
0.2%
ValueCountFrequency (%)
-18 1
 
< 0.1%
-3 5
 
< 0.1%
-2 33
 
< 0.1%
-1 1088
 
0.3%
0 194
 
0.1%
1 331365
87.4%
2 35465
 
9.4%
3 6681
 
1.8%
4 1985
 
0.5%
5 784
 
0.2%
ValueCountFrequency (%)
142 1
< 0.1%
89 1
< 0.1%
75 1
< 0.1%
66 1
< 0.1%
60 1
< 0.1%
58 1
< 0.1%
57 1
< 0.1%
52 1
< 0.1%
49 2
< 0.1%
45 2
< 0.1%

Gender
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.9 MiB
G
207062 
L
154384 
P
 
16987
U
 
635

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters379068
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowG
2nd rowG
3rd rowG
4th rowL
5th rowL

Common Values

ValueCountFrequency (%)
G 207062
54.6%
L 154384
40.7%
P 16987
 
4.5%
U 635
 
0.2%

Length

2024-06-02T22:07:22.561555image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-02T22:07:22.867547image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
g 207062
54.6%
l 154384
40.7%
p 16987
 
4.5%
u 635
 
0.2%

Most occurring characters

ValueCountFrequency (%)
G 207062
54.6%
L 154384
40.7%
P 16987
 
4.5%
U 635
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 379068
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
G 207062
54.6%
L 154384
40.7%
P 16987
 
4.5%
U 635
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 379068
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
G 207062
54.6%
L 154384
40.7%
P 16987
 
4.5%
U 635
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 379068
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
G 207062
54.6%
L 154384
40.7%
P 16987
 
4.5%
U 635
 
0.2%

Brand
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.9 MiB
TF
377916 
FP
 
1152

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters758136
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTF
2nd rowTF
3rd rowTF
4th rowTF
5th rowTF

Common Values

ValueCountFrequency (%)
TF 377916
99.7%
FP 1152
 
0.3%

Length

2024-06-02T22:07:23.059852image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-02T22:07:23.249289image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
tf 377916
99.7%
fp 1152
 
0.3%

Most occurring characters

ValueCountFrequency (%)
F 379068
50.0%
T 377916
49.8%
P 1152
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 758136
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F 379068
50.0%
T 377916
49.8%
P 1152
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 758136
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F 379068
50.0%
T 377916
49.8%
P 1152
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 758136
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F 379068
50.0%
T 377916
49.8%
P 1152
 
0.2%

Channel
Categorical

IMBALANCE 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.9 MiB
SS
190091 
LS
152260 
PT
22742 
CT
 
11909
RRL
 
1007
Other values (4)
 
1059

Length

Max length3
Median length2
Mean length2.0026565
Min length2

Characters and Unicode

Total characters759143
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCN
2nd rowCN
3rd rowCN
4th rowCN
5th rowCN

Common Values

ValueCountFrequency (%)
SS 190091
50.1%
LS 152260
40.2%
PT 22742
 
6.0%
CT 11909
 
3.1%
RRL 1007
 
0.3%
LL 655
 
0.2%
CN 263
 
0.1%
ss 72
 
< 0.1%
AZ 69
 
< 0.1%

Length

2024-06-02T22:07:23.434068image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-02T22:07:23.685162image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
ss 190163
50.2%
ls 152260
40.2%
pt 22742
 
6.0%
ct 11909
 
3.1%
rrl 1007
 
0.3%
ll 655
 
0.2%
cn 263
 
0.1%
az 69
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
S 532442
70.1%
L 154577
 
20.4%
T 34651
 
4.6%
P 22742
 
3.0%
C 12172
 
1.6%
R 2014
 
0.3%
N 263
 
< 0.1%
s 144
 
< 0.1%
A 69
 
< 0.1%
Z 69
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 759143
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 532442
70.1%
L 154577
 
20.4%
T 34651
 
4.6%
P 22742
 
3.0%
C 12172
 
1.6%
R 2014
 
0.3%
N 263
 
< 0.1%
s 144
 
< 0.1%
A 69
 
< 0.1%
Z 69
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 759143
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 532442
70.1%
L 154577
 
20.4%
T 34651
 
4.6%
P 22742
 
3.0%
C 12172
 
1.6%
R 2014
 
0.3%
N 263
 
< 0.1%
s 144
 
< 0.1%
A 69
 
< 0.1%
Z 69
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 759143
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 532442
70.1%
L 154577
 
20.4%
T 34651
 
4.6%
P 22742
 
3.0%
C 12172
 
1.6%
R 2014
 
0.3%
N 263
 
< 0.1%
s 144
 
< 0.1%
A 69
 
< 0.1%
Z 69
 
< 0.1%

Franchisee store
Text

MISSING 

Distinct392
Distinct (%)0.1%
Missing18119
Missing (%)4.8%
Memory size2.9 MiB
2024-06-02T22:07:23.985807image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length7
Median length5
Mean length5.0946837
Min length5

Characters and Unicode

Total characters1838921
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowCN1200
2nd rowCN1201
3rd rowCN1201
4th rowCN1206
5th rowCN1212
ValueCountFrequency (%)
ss222 8146
 
2.3%
ss223 6940
 
1.9%
ls751 6759
 
1.9%
ls760 6096
 
1.7%
ss283 4787
 
1.3%
ss255 4754
 
1.3%
ls758 4160
 
1.2%
ss209 4114
 
1.1%
ls741 4018
 
1.1%
ss225 3885
 
1.1%
Other values (382) 307290
85.1%
2024-06-02T22:07:24.616049image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 507600
27.6%
2 236294
12.8%
7 189211
 
10.3%
L 146372
 
8.0%
3 140869
 
7.7%
1 127982
 
7.0%
0 107281
 
5.8%
8 67568
 
3.7%
5 66767
 
3.6%
4 66621
 
3.6%
Other values (11) 182356
 
9.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1838921
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 507600
27.6%
2 236294
12.8%
7 189211
 
10.3%
L 146372
 
8.0%
3 140869
 
7.7%
1 127982
 
7.0%
0 107281
 
5.8%
8 67568
 
3.7%
5 66767
 
3.6%
4 66621
 
3.6%
Other values (11) 182356
 
9.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1838921
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 507600
27.6%
2 236294
12.8%
7 189211
 
10.3%
L 146372
 
8.0%
3 140869
 
7.7%
1 127982
 
7.0%
0 107281
 
5.8%
8 67568
 
3.7%
5 66767
 
3.6%
4 66621
 
3.6%
Other values (11) 182356
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1838921
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 507600
27.6%
2 236294
12.8%
7 189211
 
10.3%
L 146372
 
8.0%
3 140869
 
7.7%
1 127982
 
7.0%
0 107281
 
5.8%
8 67568
 
3.7%
5 66767
 
3.6%
4 66621
 
3.6%
Other values (11) 182356
 
9.9%
Distinct392
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.9 MiB
2024-06-02T22:07:24.862168image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length7
Median length5
Mean length5.095041
Min length5

Characters and Unicode

Total characters1931367
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowCN1200
2nd rowCN1201
3rd rowCN1201
4th rowCN1206
5th rowCN1212
ValueCountFrequency (%)
ss222 8550
 
2.3%
ss223 7273
 
1.9%
ls751 7103
 
1.9%
ls760 6376
 
1.7%
ss283 5021
 
1.3%
ss255 4986
 
1.3%
ls758 4359
 
1.1%
ss209 4327
 
1.1%
ls741 4245
 
1.1%
ss225 4071
 
1.1%
Other values (382) 322757
85.1%
2024-06-02T22:07:25.277745image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 532587
27.6%
2 247595
12.8%
7 198599
 
10.3%
L 153921
 
8.0%
3 148253
 
7.7%
1 134388
 
7.0%
0 113301
 
5.9%
8 71214
 
3.7%
5 69945
 
3.6%
4 69736
 
3.6%
Other values (11) 191828
 
9.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1931367
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 532587
27.6%
2 247595
12.8%
7 198599
 
10.3%
L 153921
 
8.0%
3 148253
 
7.7%
1 134388
 
7.0%
0 113301
 
5.9%
8 71214
 
3.7%
5 69945
 
3.6%
4 69736
 
3.6%
Other values (11) 191828
 
9.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1931367
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 532587
27.6%
2 247595
12.8%
7 198599
 
10.3%
L 153921
 
8.0%
3 148253
 
7.7%
1 134388
 
7.0%
0 113301
 
5.9%
8 71214
 
3.7%
5 69945
 
3.6%
4 69736
 
3.6%
Other values (11) 191828
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1931367
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 532587
27.6%
2 247595
12.8%
7 198599
 
10.3%
L 153921
 
8.0%
3 148253
 
7.7%
1 134388
 
7.0%
0 113301
 
5.9%
8 71214
 
3.7%
5 69945
 
3.6%
4 69736
 
3.6%
Other values (11) 191828
 
9.9%
Distinct575
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size2.9 MiB
2024-06-02T22:07:25.478611image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length42
Median length33
Mean length20.672948
Min length7

Characters and Unicode

Total characters7836453
Distinct characters70
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)< 0.1%

Sample

1st rowCT-BENGALURU-RESIDENCYROAD
2nd rowCT-HYDERABAD-G.S.CENTERPOINT
3rd rowCT-HYDERABAD-G.S.CENTERPOINT
4th rowCT-MUMBAI-GOREGAON-OBEROIMALL
5th rowCT-BENGALURU-J.P.NAGAR
ValueCountFrequency (%)
40027
 
5.3%
city 22369
 
3.0%
mall 18239
 
2.4%
kolkata 17513
 
2.3%
ls 12014
 
1.6%
market 10677
 
1.4%
phoenix 9901
 
1.3%
pune 7061
 
0.9%
center 6200
 
0.8%
lake 5833
 
0.8%
Other values (871) 599672
80.0%
2024-06-02T22:07:25.976078image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 629576
 
8.0%
S 566600
 
7.2%
A 491640
 
6.3%
L 475409
 
6.1%
368424
 
4.7%
a 359894
 
4.6%
T 241821
 
3.1%
R 222738
 
2.8%
N 191587
 
2.4%
O 190396
 
2.4%
Other values (60) 4098368
52.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7836453
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 629576
 
8.0%
S 566600
 
7.2%
A 491640
 
6.3%
L 475409
 
6.1%
368424
 
4.7%
a 359894
 
4.6%
T 241821
 
3.1%
R 222738
 
2.8%
N 191587
 
2.4%
O 190396
 
2.4%
Other values (60) 4098368
52.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7836453
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 629576
 
8.0%
S 566600
 
7.2%
A 491640
 
6.3%
L 475409
 
6.1%
368424
 
4.7%
a 359894
 
4.6%
T 241821
 
3.1%
R 222738
 
2.8%
N 191587
 
2.4%
O 190396
 
2.4%
Other values (60) 4098368
52.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7836453
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 629576
 
8.0%
S 566600
 
7.2%
A 491640
 
6.3%
L 475409
 
6.1%
368424
 
4.7%
a 359894
 
4.6%
T 241821
 
3.1%
R 222738
 
2.8%
N 191587
 
2.4%
O 190396
 
2.4%
Other values (60) 4098368
52.3%

MRP
Real number (ℝ)

Distinct37
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1679.6416
Minimum395
Maximum4995
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 MiB
2024-06-02T22:07:26.179580image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum395
5-th percentile499
Q1645
median1895
Q32595
95-th percentile3095
Maximum4995
Range4600
Interquartile range (IQR)1950

Descriptive statistics

Standard deviation1007.175
Coefficient of variation (CV)0.59963687
Kurtosis-0.62586443
Mean1679.6416
Median Absolute Deviation (MAD)800
Skewness0.36956364
Sum6.3669839 × 108
Variance1014401.6
MonotonicityNot monotonic
2024-06-02T22:07:26.463346image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
499 82974
21.9%
2595 52280
13.8%
645 47030
12.4%
1995 46983
12.4%
1595 30835
 
8.1%
1895 22192
 
5.9%
2695 18444
 
4.9%
3995 12883
 
3.4%
2295 8996
 
2.4%
2995 8841
 
2.3%
Other values (27) 47610
12.6%
ValueCountFrequency (%)
395 515
 
0.1%
399 193
 
0.1%
499 82974
21.9%
545 339
 
0.1%
595 5005
 
1.3%
645 47030
12.4%
845 3273
 
0.9%
895 522
 
0.1%
995 2661
 
0.7%
1195 18
 
< 0.1%
ValueCountFrequency (%)
4995 315
 
0.1%
4795 784
 
0.2%
4590 1000
 
0.3%
4190 452
 
0.1%
3995 12883
3.4%
3095 7139
 
1.9%
2995 8841
2.3%
2895 1270
 
0.3%
2795 5734
 
1.5%
2695 18444
4.9%

Gross UCP
Real number (ℝ)

Distinct332
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1970.7081
Minimum-28710
Maximum177555
Zeros194
Zeros (%)0.1%
Negative1127
Negative (%)0.3%
Memory size2.9 MiB
2024-06-02T22:07:26.783159image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-28710
5-th percentile499
Q1645
median1895
Q32595
95-th percentile4590
Maximum177555
Range206265
Interquartile range (IQR)1950

Descriptive statistics

Standard deviation1910.1406
Coefficient of variation (CV)0.96926612
Kurtosis626.27308
Mean1970.7081
Median Absolute Deviation (MAD)900
Skewness13.478237
Sum7.4703237 × 108
Variance3648637
MonotonicityNot monotonic
2024-06-02T22:07:26.962698image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
499 71971
19.0%
2595 45872
12.1%
645 41853
11.0%
1995 40929
10.8%
1595 23583
 
6.2%
1895 18954
 
5.0%
2695 16647
 
4.4%
3995 11704
 
3.1%
998 8825
 
2.3%
2295 8300
 
2.2%
Other values (322) 90430
23.9%
ValueCountFrequency (%)
-28710 1
< 0.1%
-11985 1
< 0.1%
-7990 1
< 0.1%
-7785 1
< 0.1%
-5990 1
< 0.1%
-5590 1
< 0.1%
-5390 2
< 0.1%
-5190 1
< 0.1%
-4995 1
< 0.1%
-4990 2
< 0.1%
ValueCountFrequency (%)
177555 1
< 0.1%
142125 1
< 0.1%
134775 1
< 0.1%
125790 1
< 0.1%
119800 1
< 0.1%
105270 1
< 0.1%
95700 1
< 0.1%
92510 1
< 0.1%
91590 1
< 0.1%
90915 1
< 0.1%

Net UCP
Real number (ℝ)

Distinct9883
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1776.898
Minimum-17982
Maximum127912.5
Zeros1332
Zeros (%)0.4%
Negative1133
Negative (%)0.3%
Memory size2.9 MiB
2024-06-02T22:07:27.145630image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-17982
5-th percentile499
Q1645
median1695.75
Q32395
95-th percentile3995
Maximum127912.5
Range145894.5
Interquartile range (IQR)1750

Descriptive statistics

Standard deviation1528.2469
Coefficient of variation (CV)0.86006454
Kurtosis511.42824
Mean1776.898
Median Absolute Deviation (MAD)899.25
Skewness10.408456
Sum6.7356516 × 108
Variance2335538.7
MonotonicityNot monotonic
2024-06-02T22:07:27.382951image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
499 57465
 
15.2%
645 35530
 
9.4%
2595 23745
 
6.3%
1995 22154
 
5.8%
1595 12801
 
3.4%
1895 11416
 
3.0%
1795.5 9450
 
2.5%
999 8656
 
2.3%
2695 8391
 
2.2%
3995 7567
 
2.0%
Other values (9873) 181893
48.0%
ValueCountFrequency (%)
-17982 1
 
< 0.1%
-11985 1
 
< 0.1%
-7990 1
 
< 0.1%
-6785 1
 
< 0.1%
-6380 1
 
< 0.1%
-5190 1
 
< 0.1%
-5091.5 1
 
< 0.1%
-4795 1
 
< 0.1%
-4590 3
< 0.1%
-4495 1
 
< 0.1%
ValueCountFrequency (%)
127912.5 1
< 0.1%
125705.09 1
< 0.1%
107820 1
< 0.1%
106921.5 1
< 0.1%
99800 1
< 0.1%
78045 1
< 0.1%
73381 1
< 0.1%
65934 1
< 0.1%
59940 1
< 0.1%
57942 1
< 0.1%
Distinct161
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.9 MiB
2024-06-02T22:07:27.577746image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length20
Median length17
Mean length6.9078714
Min length3

Characters and Unicode

Total characters2618553
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowBangalore
2nd rowHyderabad
3rd rowHyderabad
4th rowMumbai
5th rowBangalore
ValueCountFrequency (%)
mumbai 46993
 
12.4%
kolkata 44677
 
11.8%
bangalore 40943
 
10.8%
delhi 25910
 
6.8%
pune 24810
 
6.5%
hyderabad 19183
 
5.0%
noida 16699
 
4.4%
chennai 12188
 
3.2%
gurgaon 9426
 
2.5%
lucknow 8666
 
2.3%
Other values (106) 130715
34.4%
2024-06-02T22:07:28.045023image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 466338
17.8%
i 158281
 
6.0%
o 156486
 
6.0%
e 154444
 
5.9%
n 150344
 
5.7%
r 148286
 
5.7%
u 144671
 
5.5%
l 130077
 
5.0%
d 97983
 
3.7%
h 92844
 
3.5%
Other values (44) 918799
35.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2618553
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 466338
17.8%
i 158281
 
6.0%
o 156486
 
6.0%
e 154444
 
5.9%
n 150344
 
5.7%
r 148286
 
5.7%
u 144671
 
5.5%
l 130077
 
5.0%
d 97983
 
3.7%
h 92844
 
3.5%
Other values (44) 918799
35.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2618553
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 466338
17.8%
i 158281
 
6.0%
o 156486
 
6.0%
e 154444
 
5.9%
n 150344
 
5.7%
r 148286
 
5.7%
u 144671
 
5.5%
l 130077
 
5.0%
d 97983
 
3.7%
h 92844
 
3.5%
Other values (44) 918799
35.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2618553
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 466338
17.8%
i 158281
 
6.0%
o 156486
 
6.0%
e 154444
 
5.9%
n 150344
 
5.7%
r 148286
 
5.7%
u 144671
 
5.5%
l 130077
 
5.0%
d 97983
 
3.7%
h 92844
 
3.5%
Other values (44) 918799
35.1%
Distinct40
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.9 MiB
MAHARASTRA
83901 
WESTBENGAL
39421 
DELHI
28428 
KARNATAKA
25078 
UTTARPRADESH
23876 
Other values (35)
178364 

Length

Max length20
Median length17
Mean length10.148902
Min length3

Characters and Unicode

Total characters3847124
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKARNATAKA
2nd rowTELANGANA
3rd rowTELANGANA
4th rowMAHARASTRA
5th rowKARNATAKA

Common Values

ValueCountFrequency (%)
MAHARASTRA 83901
22.1%
WESTBENGAL 39421
10.4%
DELHI 28428
 
7.5%
KARNATAKA 25078
 
6.6%
UTTARPRADESH 23876
 
6.3%
KARNATAKA 22044
 
5.8%
WEST BENGAL 19783
 
5.2%
GUJARAT 15779
 
4.2%
HARYANA 14430
 
3.8%
TAMILNADU 11498
 
3.0%
Other values (30) 94830
25.0%

Length

2024-06-02T22:07:28.194671image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
maharastra 83901
19.3%
karnataka 47122
10.9%
westbengal 39421
 
9.1%
pradesh 28763
 
6.6%
delhi 28428
 
6.5%
uttarpradesh 23876
 
5.5%
west 19783
 
4.6%
bengal 19783
 
4.6%
telangana 19211
 
4.4%
gujarat 15779
 
3.6%
Other values (26) 108183
24.9%

Most occurring characters

ValueCountFrequency (%)
A 916792
23.8%
R 367412
9.6%
362270
 
9.4%
T 327349
 
8.5%
H 225007
 
5.8%
S 221506
 
5.8%
E 215195
 
5.6%
N 182939
 
4.8%
D 123128
 
3.2%
L 119013
 
3.1%
Other values (29) 786513
20.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3847124
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 916792
23.8%
R 367412
9.6%
362270
 
9.4%
T 327349
 
8.5%
H 225007
 
5.8%
S 221506
 
5.8%
E 215195
 
5.6%
N 182939
 
4.8%
D 123128
 
3.2%
L 119013
 
3.1%
Other values (29) 786513
20.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3847124
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 916792
23.8%
R 367412
9.6%
362270
 
9.4%
T 327349
 
8.5%
H 225007
 
5.8%
S 221506
 
5.8%
E 215195
 
5.6%
N 182939
 
4.8%
D 123128
 
3.2%
L 119013
 
3.1%
Other values (29) 786513
20.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3847124
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 916792
23.8%
R 367412
9.6%
362270
 
9.4%
T 327349
 
8.5%
H 225007
 
5.8%
S 221506
 
5.8%
E 215195
 
5.6%
N 182939
 
4.8%
D 123128
 
3.2%
L 119013
 
3.1%
Other values (29) 786513
20.4%

Billing grp
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.9 MiB
LFS
379068 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1137204
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLFS
2nd rowLFS
3rd rowLFS
4th rowLFS
5th rowLFS

Common Values

ValueCountFrequency (%)
LFS 379068
100.0%

Length

2024-06-02T22:07:28.347278image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-02T22:07:28.444889image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
lfs 379068
100.0%

Most occurring characters

ValueCountFrequency (%)
L 379068
33.3%
F 379068
33.3%
S 379068
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1137204
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 379068
33.3%
F 379068
33.3%
S 379068
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1137204
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 379068
33.3%
F 379068
33.3%
S 379068
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1137204
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 379068
33.3%
F 379068
33.3%
S 379068
33.3%

Channel_lfs
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.9 MiB
LFS
379068 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1137204
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLFS
2nd rowLFS
3rd rowLFS
4th rowLFS
5th rowLFS

Common Values

ValueCountFrequency (%)
LFS 379068
100.0%

Length

2024-06-02T22:07:28.545484image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-02T22:07:28.653956image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
lfs 379068
100.0%

Most occurring characters

ValueCountFrequency (%)
L 379068
33.3%
F 379068
33.3%
S 379068
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1137204
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 379068
33.3%
F 379068
33.3%
S 379068
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1137204
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 379068
33.3%
F 379068
33.3%
S 379068
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1137204
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 379068
33.3%
F 379068
33.3%
S 379068
33.3%
Distinct143
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.9 MiB
2024-06-02T22:07:28.803997image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length52
Median length35
Mean length16.715993
Min length8

Characters and Unicode

Total characters6336498
Distinct characters67
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st row3. 150 M Amalfi Men deo
2nd row3. 20 M Verge
3rd row8. Deo M C. Road 150 ml
4th row3. Tales W Ibiza 100 ml
5th row7. 100 W Pristine
ValueCountFrequency (%)
m 206967
 
11.9%
w 156728
 
9.0%
deo 120395
 
6.9%
ml 117070
 
6.7%
100 111533
 
6.4%
150 83167
 
4.8%
raw 64736
 
3.7%
20 57621
 
3.3%
celeste 56602
 
3.2%
1 44321
 
2.5%
Other values (115) 723276
41.5%
2024-06-02T22:07:29.111850image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1363725
21.5%
e 580247
 
9.2%
0 440882
 
7.0%
l 256745
 
4.1%
o 253746
 
4.0%
M 248651
 
3.9%
1 247972
 
3.9%
. 218260
 
3.4%
a 178549
 
2.8%
5 167853
 
2.6%
Other values (57) 2379868
37.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6336498
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1363725
21.5%
e 580247
 
9.2%
0 440882
 
7.0%
l 256745
 
4.1%
o 253746
 
4.0%
M 248651
 
3.9%
1 247972
 
3.9%
. 218260
 
3.4%
a 178549
 
2.8%
5 167853
 
2.6%
Other values (57) 2379868
37.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6336498
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1363725
21.5%
e 580247
 
9.2%
0 440882
 
7.0%
l 256745
 
4.1%
o 253746
 
4.0%
M 248651
 
3.9%
1 247972
 
3.9%
. 218260
 
3.4%
a 178549
 
2.8%
5 167853
 
2.6%
Other values (57) 2379868
37.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6336498
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1363725
21.5%
e 580247
 
9.2%
0 440882
 
7.0%
l 256745
 
4.1%
o 253746
 
4.0%
M 248651
 
3.9%
1 247972
 
3.9%
. 218260
 
3.4%
a 178549
 
2.8%
5 167853
 
2.6%
Other values (57) 2379868
37.6%

Collection
Categorical

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.9 MiB
Gift Pack
62820 
Classic 100 ml
61611 
Premium Deo
58719 
Classic 20 ml
52374 
Aqua
29753 
Other values (14)
113791 

Length

Max length15
Median length13
Mean length10.990065
Min length4

Characters and Unicode

Total characters4165982
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPremium Deo
2nd rowClassic 20 ml
3rd rowEscapade Deo
4th rowTales 100ml
5th rowClassic 100 ml

Common Values

ValueCountFrequency (%)
Gift Pack 62820
16.6%
Classic 100 ml 61611
16.3%
Premium Deo 58719
15.5%
Classic 20 ml 52374
13.8%
Aqua 29753
7.8%
Classic 50 ml 26505
7.0%
Escapade Deo 24448
 
6.4%
Escapade 19996
 
5.3%
Nox 100 ml 12883
 
3.4%
Tales 100ml 8704
 
2.3%
Other values (9) 21255
 
5.6%

Length

2024-06-02T22:07:29.248460image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ml 158620
18.0%
classic 141760
16.1%
deo 89551
10.2%
100 74494
8.5%
pack 69204
7.9%
gift 62820
 
7.1%
premium 58719
 
6.7%
20 57621
 
6.6%
escapade 44444
 
5.1%
aqua 29753
 
3.4%
Other values (13) 92344
10.5%

Most occurring characters

ValueCountFrequency (%)
500262
12.0%
a 351698
 
8.4%
s 337877
 
8.1%
l 332585
 
8.0%
m 291907
 
7.0%
0 264800
 
6.4%
i 263335
 
6.3%
c 256602
 
6.2%
e 209078
 
5.0%
C 141760
 
3.4%
Other values (28) 1216078
29.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4165982
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
500262
12.0%
a 351698
 
8.4%
s 337877
 
8.1%
l 332585
 
8.0%
m 291907
 
7.0%
0 264800
 
6.4%
i 263335
 
6.3%
c 256602
 
6.2%
e 209078
 
5.0%
C 141760
 
3.4%
Other values (28) 1216078
29.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4165982
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
500262
12.0%
a 351698
 
8.4%
s 337877
 
8.1%
l 332585
 
8.0%
m 291907
 
7.0%
0 264800
 
6.4%
i 263335
 
6.3%
c 256602
 
6.2%
e 209078
 
5.0%
C 141760
 
3.4%
Other values (28) 1216078
29.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4165982
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
500262
12.0%
a 351698
 
8.4%
s 337877
 
8.1%
l 332585
 
8.0%
m 291907
 
7.0%
0 264800
 
6.4%
i 263335
 
6.3%
c 256602
 
6.2%
e 209078
 
5.0%
C 141760
 
3.4%
Other values (28) 1216078
29.2%

Interactions

2024-06-02T22:07:14.593867image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-02T22:07:12.054280image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-02T22:07:12.753468image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-02T22:07:13.657823image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-02T22:07:14.757502image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-02T22:07:12.217836image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-02T22:07:12.992636image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-02T22:07:13.898930image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-02T22:07:14.918705image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-02T22:07:12.382001image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-02T22:07:13.230760image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-02T22:07:14.156032image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-02T22:07:15.113115image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-02T22:07:12.527204image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-02T22:07:13.427004image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-02T22:07:14.400329image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

2024-06-02T22:07:15.782934image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-06-02T22:07:17.029052image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-06-02T22:07:18.161303image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

YearMonthMonth KeyQTRRegionInvoice DateMaterialQuantityGenderBrandChannelFranchisee storeBill to Party codeBill to Party NameMRPGross UCPNet UCPBill to Party CityShiptopartyStateCodeBilling grpChannel_lfsVarient_NameCollection
02022-232022-04-01AprQ1South2022-04-01 00:00:00NFM03DQ14.0GTFCNCN1200CN1200CT-BENGALURU-RESIDENCYROAD4991996.01996.0BangaloreKARNATAKALFSLFS3. 150 M Amalfi Men deoPremium Deo
12022-232022-04-01AprQ1South2022-04-01 00:00:00NDFM13PD11.0GTFCNCN1201CN1201CT-HYDERABAD-G.S.CENTERPOINT595595.0595.0HyderabadTELANGANALFSLFS3. 20 M VergeClassic 20 ml
22022-232022-04-01AprQ1South2022-04-01 00:00:00NFM05DQ11.0GTFCNCN1201CN1201CT-HYDERABAD-G.S.CENTERPOINT499499.0499.0HyderabadTELANGANALFSLFS8. Deo M C. Road 150 mlEscapade Deo
32022-232022-04-01AprQ1West2022-04-01 00:00:00FW19PC11.0LTFCNCN1206CN1206CT-MUMBAI-GOREGAON-OBEROIMALL15951595.01595.0MumbaiMAHARASTRALFSLFS3. Tales W Ibiza 100 mlTales 100ml
42022-232022-04-01AprQ1South2022-04-01 00:00:00NEFW05PGC1.0LTFCNCN1212CN1212CT-BENGALURU-J.P.NAGAR22952295.02295.0BangaloreKARNATAKALFSLFS7. 100 W PristineClassic 100 ml
52022-232022-04-01AprQ1South2022-04-01 00:00:00NFM01DQ11.0GTFCNCN1212CN1212CT-BENGALURU-J.P.NAGAR499499.0499.0BangaloreKARNATAKALFSLFS1. 150 M Raw DeoPremium Deo
62022-232022-04-01AprQ1South2022-04-01 00:00:00NFFM05PG21.0GTFCNCN1212CN1212CT-BENGALURU-J.P.NAGAR19951995.01995.0BangaloreKARNATAKALFSLFS2. Mini 25 ml M (R,V)Gift Pack
72022-232022-04-01AprQ1West2022-04-01 00:00:00NFM02DQ11.0GTFCNCN1231CN1231CT-PIMPRI-CITYCENTREMALL499499.0499.0PuneMAHARASTRALFSLFS2. 150 M Steele DeoPremium Deo
82022-232022-04-01AprQ1West2022-04-01 00:00:00NFW02DQ11.0LTFCNCN1231CN1231CT-PIMPRI-CITYCENTREMALL499499.0499.0PuneMAHARASTRALFSLFS5. 150 W Nude DeoPremium Deo
92022-232022-04-01AprQ1North2022-04-01 00:00:00NEFM01PGC1.0GTFCNCN1239CN1239CT-NEWDELHI-WORLDMARK22952295.01951.0DelhiDELHILFSLFS1. 100 M RawClassic 100 ml
YearMonthMonth KeyQTRRegionInvoice DateMaterialQuantityGenderBrandChannelFranchisee storeBill to Party codeBill to Party NameMRPGross UCPNet UCPBill to Party CityShiptopartyStateCodeBilling grpChannel_lfsVarient_NameCollection
3790582023-242023-02-01FebQ4East2024-02-05 00:00:00FW22PC11.0LTFSSSS292SS292312-SSL-RANCHI30953095.03095.00RanchiJHARKHANDLFSLFSNoura W Iris 100 mlNoura W 100ml
3790592023-242023-02-01FebQ4East2024-02-17 00:00:00FW22PC11.0LTFSSSS292SS292312-SSL-RANCHI30953095.03095.00RanchiJHARKHANDLFSLFSNoura W Iris 100 mlNoura W 100ml
3790602023-242023-02-01FebQ4East2024-02-08 00:00:00NFFW14PK11.0LTFSSSS292SS292312-SSL-RANCHI27952795.02795.00RanchiJHARKHANDLFSLFSAqua W 90 mlAqua
3790612023-242023-02-01FebQ4East2024-02-05 00:00:00NFFW03PFL1.0LTFSSSS292SS292312-SSL-RANCHI18951895.01895.00RanchiJHARKHANDLFSLFS50 W NudeClassic 50 ml
3790622023-242023-02-01FebQ4East2024-02-15 00:00:00NFFW01CL21.0GTFSSSS292SS292312-SSL-RANCHI19951995.01995.00RanchiJHARKHANDLFSLFSCoffret Deo W CelesteGift Pack
3790632023-242023-02-01FebQ4East2024-02-12 00:00:00NFFW01CL21.0GTFSSSS292SS292312-SSL-RANCHI19951995.01995.00RanchiJHARKHANDLFSLFSCoffret Deo W CelesteGift Pack
3790642023-242023-02-01FebQ4East2024-02-05 00:00:00NFFM01PGL1.0GTFSSSS292SS292312-SSL-RANCHI18951895.01895.00RanchiJHARKHANDLFSLFS50 M RawClassic 50 ml
3790652023-242023-02-01FebQ4East2024-02-11 00:00:00NFFW03PFC1.0LTFSSSS292SS292312-SSL-RANCHI25952595.02122.32RanchiJHARKHANDLFSLFS100 W NudeClassic 100 ml
3790662023-242023-02-01FebQ4East2024-02-21 00:00:00NGFM08PC11.0GTFSSSS292SS292312-SSL-RANCHI26952695.02695.00RanchiJHARKHANDLFSLFS100 M CRoadEscapade
3790672023-242023-02-01FebQ4East2024-02-21 00:00:00NFFP01PG21.0PTFSSSS292SS292312-SSL-RANCHI19951995.01995.00RanchiJHARKHANDLFSLFSHis & Her Mini 25 ml (V,S)Gift Pack

Duplicate rows

Most frequently occurring

YearMonthMonth KeyQTRRegionInvoice DateMaterialQuantityGenderBrandChannelFranchisee storeBill to Party codeBill to Party NameMRPGross UCPNet UCPBill to Party CityShiptopartyStateCodeBilling grpChannel_lfsVarient_NameCollection# duplicates
02022-232022-04-01AprQ1East2022-04-10NEFM02PFC1.0GTFSSSS221SS221112-SSL-ELGINROADKOLKATA22952295.02295.00KolkataWESTBENGALLFSLFS2. 100 M SteeleClassic 100 ml2
12022-232022-04-01AprQ1East2022-04-15NEFW14PD11.0LTFSSSS283SS283188-SSL-ACROPOLISKOLKATA645645.0503.49KolkataWESTBENGALLFSLFS7. 20 W PristineClassic 20 ml2
22022-232022-04-01AprQ1East2022-04-28NEFW11PD11.0LTFSSSS283SS283188-SSL-ACROPOLISKOLKATA645645.0645.00KolkataWESTBENGALLFSLFS4. 20 W CelesteClassic 20 ml2
32022-232022-04-01AprQ1North2022-04-01NEFW02PFC1.0LTFSSSS216SS216121-SSL-LUCKNOW22952295.02295.00LucknowUTTARPRADESHLFSLFS4. 100 W CelesteClassic 100 ml2
42022-232022-04-01AprQ1North2022-04-02NFM01DQ11.0GTFSSSS309SS309478-SHOPPERSSTOP-TAPASYAONE499499.0499.00GurgaonHARYANALFSLFS1. 150 M Raw DeoPremium Deo2
52022-232022-04-01AprQ1North2022-04-03NEFP01PGFL1.0PTFSSSS216SS216121-SSL-LUCKNOW26952695.02695.00LucknowUTTARPRADESHLFSLFS1. His & Her 50 ml (R,C)Gift Pack2
62022-232022-04-01AprQ1North2022-04-04FM01HQ21.0GTFSSSS282SS282267-SSL-JANAKPURI18451845.01845.00DelhiDELHILFSLFS10. Amalfi Coffret M EDP + DeoGift Pack2
72022-232022-04-01AprQ1South2022-04-05NEFP01PGFL1.0PTFSSSS227SS227154-SSL-CYBERABADINORBIT26952695.02695.00HyderabadTELANGANALFSLFS1. His & Her 50 ml (R,C)Gift Pack2
82022-232022-04-01AprQ1South2022-04-28NEFM01PGL1.0GTFSSSS242SS242161-SSL-MYSORE15951595.01595.00MysoreKARNATAKALFSLFS1. 50 M RawClassic 50 ml2
92022-232022-05-01MayQ1East2022-05-02FM21PC11.0GTFSSSS303SS303314-SSL-GUWAHATICITYCENTER39953995.03995.00GuwahatiWESTBENGALLFSLFS1. Nox M 100 mlNox 100 ml2